USING STUDENT ACHIEVEMENT DATA TO SUPPORT …

1 SHARNELL JACKSON data -DRIVEN INNOVATIONS CONSULTING USING STUDENT ACHIEVEMENT data TO SUPPORT instructional decision making Welcome 2 Recommendations Action Steps Potential Roadblocks & Solutions Vetted References Practice Guide Structure 3 Input from expert panel of professors, researchers in nonprofit organizations, and practitioners Research reviewed by What Works Clearinghouse Examined hundreds of articles (2,853 495 64 24 6) Recommendations Peer review 4 Development Process 4 Practice Guides: Levels of Evidence Strong Moderate Low Strong causal evidence AND strong generalizable evidence Either strong causal evidence OR strong generalizable evidence Supported by expert opinion, based on evidence that does not rise to the moderate level Our recommendations have a low level of evidence, but there is extensive SUPPORT from descriptive studies, case studies, and surveys.

2 5 Scope of this guide is limited to typical assessment data , but readers should consider how to integrate data from multiple sources. Consider all five recommendations as a research-based framework for effective data use that requires coordinated, systemic mindset efforts across all levels of the education system. Research cannot measure what has not been implemented broadly or deeply. Research must mirror the practice then the body of rigorous research will grow. Notes on the guide 6 USING data : Continuous Improvement Framework Leadership data Coaches data Teams Establishing a data Culture Technology Use 7 How well can the teacher access and act on data to inform instructional decisions?

3 8 The process by which an individual collects, examines, and interprets empirical evidence for the purpose of making a decision . What is data -driven decision making ? 9 Pieces of information data are meaningless by themselves and given meaning through the context in which they occur in instruction Context transforms data into information that is actionable to a decision -maker Educational data may be demographic, financial, personnel, annual, interim, or classroom-level 10 What are data ? data exist in a raw state without meaning Information data given meaning in context Knowledge collection of information deemed useful to guide action Fundamentals about data : The data Continuum 12 Technological advances, a proliferation of assessment data , and human capacity issues data and reports are increasingly accessible Need to promote appropriate use Growing recognition of the need to personalize instruction to address ACHIEVEMENT gaps and meet accountability targets to help all children learn Policy and compliance requirements Why focus on data use?

4 13 Our best teachers today are USING real time data in ways that would have been unimaginable just five years ago. They need to know how well their students are performing. They want to know exactly what they need to do to teach and how to teach it. (Duncan, 2009) data and data analyses are powerful tools that must be used to improve schools. (Easton, 2009) Quotes to set the stage 14 Identify possible solutions Provide continuous monitoring Use data to identify a problem Target research to examine the impact Impact Problem Solution Monitor Bryk, Sebring, Allensworth, Luppescu, and Easton, (2010). Organizing Schools for Improvement.

5 The University of Chicago Press. Continuous improvement process 15 Collect and prepare a variety of data about STUDENT learning Interpret data and develop hypotheses about how to improve STUDENT learning Modify instruction to test hypotheses and increase STUDENT learning data Use Cycle 16 USING STUDENT ACHIEVEMENT data to SUPPORT instructional decision MakingUsing STUDENT ACHIEVEMENT data to SUPPORT instructional decision MakingNCEE DEPARTMENT OF EDUCATIONIES PRACTICE GUIDEWHAT WORKS CLEARINGHOUSEW here does data come from now? 17 How quickly can a teacher use the data to personalize instruction? No single assessment provides all the information teachers need to make informed instructional decisions Demographic data Perception data School Processes data STUDENT Learning data 18 All four areas must be considered.

6 Collect and prepare a variety of data about STUDENT learning by: focusing on specific questions about STUDENT ACHIEVEMENT prioritizing which types of data to gather to inform instructional decisions USING multiple sources of data : no single assessment provides all the information teachers need to make informed instructional decisions collecting and preparing classroom performance data for examination 19 Multiple Measures of data to Drive Personalized Learning Needs Personalizing Learning Needs Classwork, Quizzes, Portfolios Benchmark Assessments IEPs, Attendance, Behavior Interim Assessments Diagnostic Assessments Formative Assessments Common Schoolwide Assessments 20 ANNUALLY Summative National, State, District, School Aggregated, disaggregated, strand, and items SEMI- ANNUALLY data About People, Practices, Perceptions Demographic, enrollment, attendance.

7 Behavior, surveys. Interviews, observations, curriculum maps, parent meetings, phone calls, and cumulative folders QUARTERLY Benchmark School-wide Common Assessments End of unit, common grade and subject-level tests reported at the item level analysis, units, modules, portfolios MONTHLY Formative School-wide Common Assessments Math problems, writing samples, science journals and lab work, class projects, blogs, wikis, and other STUDENT work WEEKLY Formative Classroom Assessments for Learning STUDENT self assessments, descriptive feedback, selected response, written response, personal communications, performance assessments, class work, homework, and running records 21 School data Use One of the potentially powerful resources for informing instructional and school improvement school-wide data is enormously underutilized.

8 The distinguishing characteristics of school-wide data are that they are frequently and systematically collected across a grade level or content area about an important STUDENT outcome and quickly aggregated and examined for patterns that can help inform next steps. Supovitz, , Klein, V. (2003). Mapping a Course for Improved STUDENT Learning: How Innovative Schools Systematically Use STUDENT Performance data to Guide Improvement. Philadelphia: Consortium for Policy Research in Education, University of Pennsylvania. 22 Identify a promising intervention or instructional modification and an effect that you expect to see Ensure that the effect can be measured Identify the comparison data 23 Characteristics of testable hypotheses Identifying STUDENT Learning Problems 24 Levels of data Types of data (Who?)

9 What? Based on what evidence?) Aggregated results Sixty-five percent of all sixth grade students passed the physical science assessment. Disaggregated results There is a persistent ACHIEVEMENT gap between White and Latino students in science; this year s gap was 28% Strand results N/A (the assessment focused on one strand area) Item results students performed poorly on the 10 items assessing buoyancy, with an average of 22 percent proficient; six of these items asked students to predict which objects would either float or sink. students work Showed evidence of misconceptions with the concept of buoyancy and how the composition of an object relates to its buoyancy.

10 24 USING the formative assessment process as feedback to close ACHIEVEMENT gaps and inform instruction Allocating more time for struggling students to close gaps Reordering the curriculum to shore up essential skills Designating particular students to receive additional SUPPORT Attempting new ways of teaching complex concepts Better aligning performance expectations among performance standards, outcomes, and classrooms Better aligning curricular emphasis among grade levels based on data and item analysis Modify instructional changes to raise STUDENT ACHIEVEMENT 25 Principles for Personalizing Learning Collect mul+ple forms of forma+ve assessment data about STUDENT learning to verify causes that will determine next instruc+onal problems and steps.